University adopts predictive technology

By Cynthia Karena

3 November 2011 — 1:12pm

Siri was not the first speech recognition application to grace our phones, but its success will help increase expectations for more artificial intelligence in business.

Siri combines speech recognition and natural language processing with artificial intelligence and search algorithms in the cloud to behave like an intelligent human assistant. It stops short of predicting, but it sounds like it does.

Even more predictive technologies are starting to be applied to business applications to help us wade through the seemingly unsurmountable volume of data compounding every day.

Edith Cowan University in Perth is using a predictive model developed by IBM to identify students at risk of leaving a course before they finish.

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"People have hunches and gut feelings about the reasons for attrition, but now it's an evidence based view," says IBM's Graham Kittle, practice leader of the business analytics and optimisation team in Australia and New Zealand.

Rather than answering simple questions such as 'how many students are enrolled', IBM's predictive model can answer more complex questions such as 'which students are showing signs of needing extra support?' and 'what are the causes?'

"The university can intervene when a flag goes up," says Kittle, "for example, low performance over one semester."

The trick in a project like this is to navigate the huge amounts of data available. Kittle says there are over 100 variables, including information modelled on previous students' grades and attrition rates.

In future, it will include more indicators for a better model to predict attrition, as well as to best identify which students are likely to be successful in further postgraduate study.

"(Similarly) my behavior, activities and likes could be used to make predictions about me, and what choices I may make. Amazon does this with book recommendations, though it's pretty simplistic.

"We at NICTA are investigating similar things in health - can we predict the likely risk of contracting a disease, the likely length of stay, or likely future hospital admissions, from patient information?"

Before he joined NICTA, Cavedon worked with some of the Siri developers on the US Defense Advanced Research Projects Agency's CALO (Cognitive Assistant that Learns and Organizes) project.

"Siri doesn't recognise patterns of behavior," says Cavedon. "It knows about you from what's on your phone – your contacts and calendar entries. It has some simple artificial intelligence (but) it doesn't (yet) make predictions based on past behavior."

But what could Siri do if it had predictive capabilities?

"Say I always book an outdoor table at a restaurant near the beach on Friday evenings when it's sunny and warm. A prediction system could notice that pattern and ask me if I wanted to do specifically that on a given warm sunny Friday, and maybe make a specific recommendation based on what my closest Facebook friends have recently 'liked'

"Siri can look up external sources to recommend a restaurant, but it would be nicer if Siri could also take note of what my friends are recommending on Facebook. That would be cool."

One of the specific challenges in using information from social media is that the information is in text format, not structured databases, says Cavedon.

"At NICTA, we are investigating how to perform text analytics (rather than data analytics) across different types of text documents, from tweets to research papers."

Speech Recognition

Siri could also be doing tasks based on voice command, says futurist Ross Dawson.

"We are heading towards more natural speech recognition and response. But the more useful thing is better human-machine interfaces. We're still using a mouse to interact, exactly what we were doing decades ago.

"We're on the verge of a significant transition to better human-machine interaction so (machines) understand what we mean. And voice is a big part of that. There are also gestures, eye gaze, facial expressions and even thoughts. But voice is one of the most important, as it's generally the most efficient and is the least effort for communication."

When people control devices with speech, the power lies in the system understanding the person, says Dawson, "what they mean or what they want by making statements".

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In future more business applications may be able to answer verbal questions such as "what are my stock levels today?", or "tell me how sales are going across the globe" more readily than the hours it takes to navigate and analyse myriad reports.